Is Rotation a Nuisance in Shape Recognition?
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Is Rotation a Nuisance in Shape Recognition? Qiuhong Ke1;2 Yi Li2;3 1Beijing Forestry University, 2NICTA, 3ANU [email protected], [email protected] Abstract example, where a shape is uniformly sampled and repre- sented by a series of high dimensional feature vectors. Ro- Rotation in closed contour recognition is a puzzling nui- tation amounts to shifting these vectors in a circular order, sance in most algorithms. In this paper we address three which makes the comparison of two shifted series difficult, fundamental issues brought by rotation in shapes: 1) is because this mis-correspondence phenomenon is not han- alignment among shapes necessary? If the answer is “no”, dled inherently by nearest neighbor methods and classifiers. 2) how to exploit information in different rotations? and Documented solutions of rotation invariance include: 1) 3) how to use rotation unaware local features for rotation define domain specific global rotation angle (a.k.a. “major aware shape recognition? axis”), 2) adopt rotation invariant representation of local de- We argue that the origin of these issues is the use scriptors (e.g., histogram), and 3) brute force search. Global of hand crafted rotation-unfriendly features and measure- rotation angle may be useful for the limited domains, how- ments. Therefore our goal is to learn a set of hierarchi- ever it is also believed that the major axis is not reliable cal features that describe all rotated versions of a shape as [16]. Many solutions frame the shape matching problem as a class, with the capability of distinguishing different such a histogram comparison problem [12]. Unfortunately, use- classes. We propose to rotate shapes as many times as pos- ful information such as structure has to be discarded during sible as training samples, and learn the hierarchical feature histogram computation in order to achieve rotation invari- representation by effectively adopting a convolutional neu- ance in this process. Trying all possible rotations appears to ral network. We further show that our method is very effi- be the last resort, but this is at the expense of efficiency [8]. cient because the network responses of all possible shifted With all the attempts to achieve rotation invariance, it is versions of the same shape can be computed effectively by interesting to note that rotation invariance may even not be re-using information in the overlapping areas. We tested the favorable in some domains. For example, rotation invari- algorithm on three real datasets: Swedish Leaves dataset, ance techniques cannot differentiate between the shapes of ETH-80 Shape, and a subset of the recently collected Leaf- the lowercase letters “d” and “p”, since this pair of shapes snap dataset. Our approach used the curvature scale space only differs in their orientations. Therefore, “rotation limit” and outperformed the state of the art. or “rotation awareness” may be more practical to improve recognition accuracy in these applications. All these difficulties attest that rotation is a nuisance in 1. Background shape recognition. Researchers frequently need to make se- Matching closed contour shapes is an important problem ries decisions of the following three puzzling questions: 1) in computer vision. Dated back to the early age, where ap- shall we rotate shapes? 2) shall we use histogram? and 3) plications are anthropology and biometric [15], it has novel shall we use rotation invariant local feature? The answers applications in domains as diverse as computer graphics and appear elusive, but first of all, why we have these questions? mobile computing [9]. The origin that rotation becomes a bitter and resentful Typical issues in shape recognition include noise, scale, enemy is that we hand craft orientation-unfriendly repre- translation, and rotation. Most of them can be handled sentations and measurements. For instance, representing easily either by carefully chosen classifiers, different data 2D circular points by 1D vectors and feeding them to Sup- representations, and simple normalizations. However, re- port Vector Machine already abandon any hope of han- gardless various techniques are used, rotation invariance dling the rotation, because kernels are hardly shift invari- in shape recognition is particularly difficult to achieve [6]. ant. Even the widely used dynamic time warping is not Take the widely used “landmark point” representation for rotation-friendly, because it neither encodes global mea- 1 • We outperformed state of the art methods in real datasets. 2. Related work Shape is one of the important features in computer vi- sion. Particularly we focus on the closed contour, where landmark points are obtained by uniformly sampling the contours. We review three components in shape recogni- tion: features, distance measurements, and invariance. Figure 1. Two time series representations of the same shape, start- ing from two different contour points. Features Local descriptors can be rotation dependent or independent. In the early age, a large number of papers surement that describes all possible rotations between two extract rotation invariant features such as curvature, entropy, shapes, nor exploits the rotation property. and convex hull [5]. These rotation invariant features are Instead of defining the representations manually, can we less distinctive in literature. learn a set of features for the same shape at all possible rota- Alternatively, Shape Context [2] and Inner Distance tions and use them for distinguish different shapes at differ- Shape Context [12] are two widely used descriptors that ent rotations? In this paper we argue that we can tackle this depend on rotation. In shape recognition, they are usually challenging concerns drastically in one shot. We consider used in dynamic programing framework. Please note that shapes along boundaries as 1D time series and use convo- such descriptors can be modified to be rotation independent lutional neural network [3] as a practical solver, because by computing the “local” orientation, but it is not a common filtering is particularly suitable for time series analysis. practice. In this paper we further suggest that adding more rotated versions of shapes improves shape recognition by nature. In Distance There are many distance measures for shapes. this case, the training will be time consuming with the brute When two point sets are used, Chamfer distance [4] is fre- force solution. While in contrast, we significantly improve quently used in the 2D space measures. the speed without losing accuracy. First, we take a slow but Experiments suggest that 1D representations (time se- intuitive approach to explain the problem, and then we de- ries) achieve superior accuracy. For time series, typical scribe how we make the training and testing more efficient. distance measurements include l2 norm and Dynamic Time Imagine we manually circular shift the time series and Warping [12]. Typically there are implicit assumption to collect all shifted versions as training samples, this naive define starting point [4], thus one may need to check all implementation is unsatisfactory for many scenarios. For possible circular shifts to find the optimal matching. example, it takes 200 times to train a network if a shape is To avoid time consuming distance comparison, align- sampled at 200 points. ment or brute force search may be used to produce the best We demonstrate that we can reduce the complexity dras- accuracy in different domains. tically. The key ingredient is to reuse the redundant in- formation between two shifted versions (Fig. 1). In the Rotation invariance With the elusive starting point, ro- case where average pooling is used, this information reusing tation invariance seems to be always hard to handle in the strategy forms a balanced binary tree. Thus, all responses literature. Major axis appears to be the dominant choice in of the same shape may be computed effectively. This also some areas, but even in Anthropology, a classical area of allows us to perform the “rotation limit” cases. shape matching, [8] argued that the major axis is not useful. We adopt curvature scale space (CSS) as our local de- The points can be manually chosen for training samples, but scriptor [13]. The curvature is a good representation of it can be labor intensive for large dataset [7]. the shape, but numerical method of curvature calculation Histogram is an efficient approach to achieve rotation in- is sensitive to noise. Therefore, we applied integral mea- variance. Kumar et al. [9] concatenated the histogram of the surements proposed by [9] as the curvature of contours. curvatures across multiple scales and achieved impressive Our contributions include results on a large dataset. The drawback is that structural • We introduced an efficient algorithm for shape recog- information may be lost during this histogram computation. nition, based on the recent progress of deep learning. While rotating all shapes is necessarily in some applica- tions, it may be useful to express rotation-limited queries. • The algorithm naturally handles rotation invariance For example, allowing a maximum rotation in a shape or and rotation limit cases. shift in landmark points. Figure 2. Shift-Delay Forward Operation illustrated. 3. Learning rotation robust features for shape signals. In the following derivation, we ignore the bias term, recognition but it can be added using the same idea. We first briefly introduce basic concepts in the Convolu- Shift-delayed Forward Operation tional Neural Network (CNN), and then present our efficient implementation of handling all possible shifts of shapes. Let us consider 1D time series representation for shapes. We first show a slow and naive approach, and then greatly 3.1. Convolutional Neural Network speed it up. The higher dimensional time series can be han- Convolutional Neural Network (CNN) is a multilayer dled using the same principle. learning framework, which may consist of an input layer, In order to generate all possible shifts of a time series, we a few convolutional layers and an output layer for logistic have to shift one curve n times in a circular order, where n is regression.